A major limitation of existing ECTS methods is their reliance on inputs being regularly-spaced; they decide whether or not to halt at each possible timestep.
This does not account for missing values or gaps between observations,
features essential to classifying ITS [1]}.
In ITS, the gaps between consecutive observations may even be large and
unpredictable, so waiting until the next value arrives has consequences. Furthermore, many ITS are multivariate, and multiple variables are rarely observed concurrently.
This compounds issues with existing ECTS methods.
Further, the times at which observations arrive can itself provide valuable knowledge for accuracy [2]} and earliness.
A successful solution to our problem should take advantage of this extra source of information.
